Sean Raftery

Introduction

Hello! As a mechanical reliability engineer with 18 years of experience, I specialize in ensuring the safety, efficiency, and long-term performance of mechanical systems. My work focuses on analyzing failures, improving maintenance strategies, and applying data-driven methods to extend equipment life and reduce downtime. I grew up in Chicago, Illinois and have also lived in Wisconsin, Indiana, Michigan, and now Alabama.

Academic Background

Professional Background

Currently I am a Senior Reliability Engineer at ArcelorMittal Calvert in their Hot Dip Galvanizing Lines. My day-to-day work includes:

  • Equipment Monitoring & Analysis
  • Failure Investigation (Root Cause Analysis)
  • Maintenance Strategy Development
  • Collaboration with Operations & Maintenance Teams

My career has progressed across multiple areas of flat-rolled carbon steel manufacturing, beginning in operations, advancing through maintenance, and now focusing on engineering. I have worked with leading producers including U.S. Steel and Cleveland-Cliffs, gaining extensive experience with steel finishing equipment, project execution, while driving continuous improvement initiatives.

Experience with R

While my experience with the R programming language is currently limited, I am eager to learn and expand my skills. I am focused on developing proficiency in R to analyze the extensive operational data we generate at ArcelorMittal Calvert

My work has provided me with a strong foundation in data analysis and problem-solving, which I believe will be highly valuable in this course.

Experience with Other Analytic Software

In 2024, I completed a database management course and gained hands-on experience with SQL, learning how to efficiently manage and query large datasets.

I also have experience with:

  • Excel – advanced functions and Power Query to streamline reporting processes
  • Tableau – creating interactive dashboards to communicate insights effectively

These tools have allowed me to approach problems from multiple angles and select the most effective solution based on the complexity and scale of the data. I’m hoping later in my M.S. degree program I’ll be able to learn and become proficient in Python

Understanding Reliability Metrics

The top three reliability metrics, Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), and Availability, are crucial for a comprehensive understanding of a system’s performance and operational efficiency. By analyzing these three metrics together, organizations can gain a comprehensive view of a system’s reliability, maintainability, and overall performance, enabling them to make informed decisions about maintenance schedules, system upgrades, and resource allocation to optimize operational uptime and reduce costs.

Abbreviation Full Name Description Formula
MTBF Mean Time Between Failure Represents the average time a system, component, or piece of equipment operates before failure, calculated by dividing total operating time by the number of failures. A higher MTBF indicates greater reliability, while a lower MTBF suggests more frequent failures and increased maintenance needs. \(MTBF = \frac{\text{Total Operating Time}}{\text{Number of Failures}}\)
MTTR Mean Time to Repair Represents the average time required to restore a system, component, or equipment to full operation after a failure, calculated by dividing total downtime by the number of repairs. A lower MTTR reflects quicker recovery and higher maintainability, while a higher MTTR indicates longer downtime and greater risk of operational disruption. \(MTTR = \frac{\text{Total Downtime}}{\text{Number of Repairs}}\)
A Availability Represents the probability that a system or component is operational and ready to perform its function at a given time, reflecting the percentage of uptime. It depends on both reliability (failure frequency) and maintainability (repair speed), with high availability indicating a dependable system with minimal downtime. \(A = \frac{MTBF}{MTBF + MTTR}\)